Key Concepts in Distributed Systems: Middleware, RPC, Mobile Agents, Consistency, Transactions, and Coordination Algorithms

Middleware in distributed systems provides a software layer that abstracts heterogeneous computers and networks into a single logical system, offering transparency and combining the scalability of network operating systems with the usability of distributed operating systems.

The basic steps of RPC communication are outlined: the client calls a stub, the stub creates a message and invokes the local OS, the message is sent to the remote OS, the server stub extracts parameters, invokes the server, the server processes the request and returns the result through the server stub, which packages the result and sends it back to the client stub, which finally returns the result to the calling process.

Mobile agents are characterized by autonomy, reactivity, goal‑orientation, reasoning/learning/adaptability, mobility, and social interaction.

Naming services for mobile entities can be implemented using broadcast/multicast, forwarding pointers, assigning initial positions, or building a search tree.

Petri‑net modeling of the classic dining‑philosophers problem is described, showing places for hungry, thinking, eating states and forks, tokens representing current states, and transitions that move philosophers between states.

Consistency models are presented, including client‑centric models (monotonic‑read, monotonic‑write, read‑your‑writes, writes‑follow‑reads) and data‑centric models (strict, sequential, linearizable, and causal consistency), with their definitions and properties.

Code migration in distributed systems is explained as process migration, with classifications of weak versus strong mobility and sender‑initiated versus receiver‑initiated migration.

Transaction commit protocols are covered: the two‑phase commit (2PC) protocol with its coordinator and participant steps, and the three‑phase commit (3PC) protocol that adds a pre‑commit phase to avoid blocking when failures occur.

Election algorithms, such as the bully algorithm, are detailed, showing how processes with higher identifiers take over the election and eventually the highest‑ID process becomes the coordinator.

Mutual‑exclusion algorithms are discussed, including centralized coordinator‑based algorithms, distributed algorithms using timestamps and message exchange, and token‑ring algorithms, each with their advantages and drawbacks.